Imaging AI

AI Revolution in Medical Imaging

Latest developments in predictive analytics, and democratization of AI tools have led to a revolution in medical diagnosis. The benefits have trickled down to the large communities of academia, budding students and professionals who would have been otherwise excluded from contributing to this domain, and millions of enthusiasts like us, who might have remained ignorant of this amalgamation of these seemingly unrelated disciplines - computer science, statistics, and medicine.


Much less visible to the public eye are the clinical imaging companies, which have created their entire service models around the ongoing AI revolution. These organizations rely on inhouse resources to develop and validate the technology behind the models. But these organizations are still far from removing the complete reliance of medical specialists in detecting ailments, but are proving to be competent assistants. The FDA also has taken effective measures to regulate the AI based Software as a Medical Device (SaMD), especially to assess the risk to patients, ensuring transparency in the complex process of AI enabled process product development.

Arguably there is no place better than open data science competition platforms such as Kaggle, Drivendata etc.  For example, Open Source Imaging Consortium (OSIC) launched a competition on Kaggle to test various Artificial Intelligence algorithms to detect Pulmonary Fibrosis Progression. The competition is still going on going, with more than 1000 teams participating. The ailment has no known cause or cure, which makes it for a very challenging prognosis. In order to find a solution, OSIC ensured that the problem statement reached to a larger audience with a diverse skillset. OSIC has provided the participants with huge datasets that comprise of patient data such as patient condition, lung capacity, smoking status etc.

Another interesting competition that took place recently on Drivendata is Clog Loss: Advanced Alzheimer’s Research, which is sponsored by a science community – Stall Catchers. The competition was about developing a machine learning model to detect clogged blood vessels in the brain, which could be the reason behind the Alzheimer’s disease. The 3D image stack is provided by on 3D image stacks provided by Cornell University’s Department of Biomedical Engineering, which was to be used to train and test the AI models.

At Choice TeleMed, we have the expertise to enable AI based medical imaging companies to develop or integrate existing highly performant technology-based tools and navigate effectively through the complexities of medical regulations by leveraging our wide network of specialist medical doctors. If you are thinking about integrating AI into your Imaging workflow or incorporating AI read into your clinical trial (for a variety of reasons) our team of experienced professionals would be delighted to have a chat.

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